To choose a product, investors tend to rely on a few easy-to-grasp variables, like recent performance and, increasingly, fees. In the case of factor investing, the explosion in the number of smart beta products has clearly put the spotlight on this second aspect. Yet focusing too much on fees can be counterproductive.
The widespread adoption of factor investing has been largely driven by the success of generic products, often labeled ‘smart beta’ and marketed as index funds of exchange-traded funds (ETFs). A key reason behind this success is low fees. Over the past decade, investors have increased their scrutiny on this aspect, as they became more aware of the impact fees have on their long-term returns.
Smart beta did not escape unscathed. Morningstar’s 2019 US fund fee study noted that the ‘fee war’ that has been raging for years among capitalization-weighted index funds has now spread into other segments, including smart beta. The study also said that while smart beta ETFs’ fees remain higher than those of traditional index funds, they are now much lower than those of active funds.
The virtues of index-based, low-expense investment products have been abundantly publicized by prominent voices of the investment community – and reported in influential scientific publications.1 As Princeton University professor and best-selling author Burton Malkiel put it in a 2017 interview with Robeco, investors should “control the things [they] can, and one of those things is costs.”
Fee scrutiny has become so intense that ETFs are now commonly regarded as an attractive, low-cost alternative to actively managed mutual funds, despite the lack of empirical evidence to support this claim. Actually, recent Robeco research suggests the average ETF investor is, in fact, not necessarily better off. This is also the case for those investing in factor ETFs.2
Surely, while this push towards more cost-efficient factor investing products has enabled considerable savings for investors, in many cases it has also been to the detriment of the quality of the solution. This is especially true for factor investing. Indeed, while generic products on smart beta indices have merits, including their high transparency and low fees, they also come with serious drawbacks.
These drawbacks have been highlighted in numerous empirical studies.3 For instance, many of these products provide only limited exposure to a targeted factor, or combination of factors, as well as unwanted negative exposures to other proven factors. This is because individual factors can have negative exposures to other proven factors, and generic factor strategies tend to overlook this issue.
Smart beta products also often use inefficient index construction processes that can result in unnecessary turnover, high concentration in some countries, sectors or industries, or excessive exposure to large-capitalization securities. In addition, they are prone to overcrowding and arbitrage, partly due to their naive trading rules, where transactions are concentrated on just a handful of rebalancing dates every year, and entail a complete lack of capacity control.
Finally, another important aspect is that the methodology of smart beta strategies is set in stone, which means that they cannot be adjusted to take into account incoming research insights, nor the potentially changing needs and priorities of clients. Yet what was state -of the art a decade ago is usually no longer state of the art today.
Ultimately, the debate concerning the importance of fees in the product-selection process is not so much about fee levels in absolute terms, but rather about which solutions provide the highest long-term net returns. In other words, which solutions provide the most efficient factor exposure and the best risk management per basis point of fee. Unfortunately, measuring this is easier said than done.
Even when tapping very similar well-known sources of returns, factor portfolios can be constructed in many ways. For example, a value strategy can be based on the book-to-market ratio, but also on the earnings-to-price ratio or the free cash-flow-to-price ratio. Similarly, buy-and-sell rules of a strategy can also be defined in numerous ways.
These examples show that implementing factor investing is definitely not a binary decision. Once they have decided to go for it, investors inevitably have to make a number of choices, either explicitly or implicitly,4 which explains why different factor solutions typically lead to very different investment outcomes.5
Publicly available empirical studies that compare the factor exposures provided by ETFs with those provided by active mutual funds remain scarce.6 And the evidence reported so far is mixed. While the best mutual funds appear to provide higher factor exposure than comparable ETFs, a significant portion of the active factor products on offer clearly fails to deliver the expected level of factor exposure relative to cheaper ETFs.
The decision whether to opt for index-based or active strategies, in order to obtain the desired exposures to factor premiums, requires a careful and thorough cost-benefit assessment of all the options available. Every investor should make this assessment individually, naturally taking into account costs – including fees.
So, while fees may be key for product selection, they must never be considered in isolation. Any potential fee reduction can very easily be wiped out by other elements, such as unintended negative exposures to proven factors, or simply due to arbitrage activity on public factor indices. Table 1 sums up four common pitfalls of generic strategies and the solutions active factor managers can offer.
In this context, well-designed active factor strategies can be worth the higher fees they entail relative to index-based products, as their enhanced approach have the potential to generate higher and smoother long-term outperformance. Yet periods of lagging performance relative to the market capitalization-weighted index remains inevitable, except for those investors successful enough to time their factor exposures. The debate over tactical factor timing will be addressed in the following article of this series.
1 See for example: Sharpe, W.F., 1991, ‘The Arithmetic of Active Management’, Financial Analysts Journal. See also: Bogle, J. C., 2014, ‘The Arithmetic of “All-In” Investment Expenses’, Financial Analysts Journal.
2 Blitz, D. C., Vidojevic, M., 2019, ‘The performance of Exchange-traded funds’, working paper.
3 See for example: Gushkov, D., 2016, ‘How Smart Are Smart Beta Exchange-Traded Funds? Analysis of Relative Performance and Factor Exposure’, Journal of Investment Consulting. See also: Blitz, D.C., 2016, ‘Factor Investing with Smart Beta Indices’, working paper. See also: Amenc, N., Goltz, F., Lodh, A., and Luyten, B., 2018, ‘Measuring factor exposure better to manage factor allocation better”, Scientific Beta Publication.
4 For an extensive discussion, see for example: Israel, R., Jiang, S. and Ross, A., 2017, ‘Craftsmanship alpha: An application to style investing’, The Journal of Portfolio Management.
5 For an illustration, see Van Gelderen, E. and Huij, J., 2014, ‘Academic knowledge dissemination in the mutual fund Industry: can mutual funds successfully adopt factor investing strategies?’, The Journal of Portfolio Management.
6 See, for example, Rabener, N., 2018, ‘Factor exposure: smart beta ETFs vs. mutual funds’, factor research note.
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